text: # Copyright 2020 Huawei Technologies Co., Ltd
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  # http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ============================================================================
  """
  ######################## eval lenet example ########################
  eval lenet according to model file:
  python eval.py --data_path /YourDataPath --ckpt_path Your.ckpt
  """
  
  import os
  from src.model_utils.config import config
  from src.model_utils.moxing_adapter import moxing_wrapper
  from src.dataset import create_dataset
  from src.lenet import LeNet5
  
  import mindspore.nn as nn
  from mindspore import context
  from mindspore.train.serialization import load_checkpoint, load_param_into_net
  from mindspore.train import Model
  from mindspore.nn.metrics import Accuracy
  
  
  def modelarts_process():
      config.ckpt_path = config.ckpt_file
  
  @moxing_wrapper(pre_process=modelarts_process)
  def eval_lenet():
      print('eval with config: ', config)
      context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target)
  
      network = LeNet5(config.num_classes)
      net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
      # repeat_size = config.epoch_size
      net_opt = nn.Momentum(network.trainable_params(), config.lr, config.momentum)
      model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})
  
      print("============== Starting Testing ==============")
      param_dict = load_checkpoint(config.ckpt_path)
      load_param_into_net(network, param_dict)
      ds_eval = create_dataset(os.path.join(config.data_path, "test"),
                               config.batch_size,
                               1)
      if ds_eval.get_dataset_size() == 0:
          raise ValueError("Please check dataset size > 0 and batch_size <= dataset size")
  
      acc = model.eval(ds_eval)
      print("============== {} ==============".format(acc))
  
  
  if __name__ == "__main__":
      eval_lenet()
  